Difference between revisions of "ANLY482 AY2016-17 T1 Group2: Project Overview"

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! style="vertical-align:top; padding:0px;" width="50%" | [[ANLY482_AY2016-17_T1_Group2: Project Overview | <font color="#212121">Background</font>]]
 
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! style="vertical-align:top; padding:0px;" width="50%" | [[ANLY482_AY2016-17_T1_Group2: Project Data | <font color="#757575">Data</font>]]
 
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<div style="background:#0096da; line-height:0.3em; font-family:sans-serif; font-size:120%; border-left:#bbdefb solid 15px;"><div style="border-left:#fff solid 5px; padding:15px;"><font color="#fff"><strong>Motivation</strong></font></div></div>
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In today’s digitalised world, technology had greatly transformed gambling behaviour. Customers no longer have to go to the physical outlet to check the odds and place their bets, they are able to do so in their comfort of their house.
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As TixCo is the only authorised ticketing service provider for certain events, it is crucial for them to cater to the demand of the mass public. Currently, TixCo is unable to anticipate the demand for the event organised and this post challenge for TixCo to fully capture the demand efficiently. As such, any uncaptured demand is an opportunity lost for TixCo. Hence, it is important for them to understanding the demand for an event.
  
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Singapore Pools allows their customers to register an online account with them and then customers are able to place their bet via phone call. With this, customers are able to place their bets without being constrained by the opening hours of Singapore Pools’ physical outlet. Hence, Singapore Pools has to allocate resources, such as phone-betting officer, to assist their customers to place their bets.
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For this, TixCo will need to:
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* Understand the trend and pattern of the number of tickets sold per event
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* Identifying the possible bottleneck for demand
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The current process of resources allocation for phone betting is based on the rough estimation provided by the Sports Division. As a result, resources are often not optimised. Moreover, due to time difference, the more well-known leagues such as English Premium League, are often broadcasted in the middle of the night. This makes it difficult to allocate additional resources at the last moment to handle the demand.
 
  
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With these challenges, it can prove costly to Singapore Pools as they are unable allocate their resources efficiently to deal with the demand and the affected customers will potentially resort to illegal counterparts to place their bets.  
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Hence, Singapore Pools hope to utilize their historical data to derive insights and predict the demand for future matches, so that they are able to better allocate their resources.
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The objective of this project is to:
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* Examine the underlying factors that affect the number of tickets sold per event
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* Understanding the distribution of the number of tickets sold per event
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* Understanding the relationship between the other attributes and the number of tickets sold per event
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The project team also aims to develop an appropriate model to predict the number of tickets sold per event, based on the historical data provided by TixCo.
 
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The objective of this project is to build a predictive model for Singapore Pools’ Sports Division staff to predict the demand of soccer matches from different leagues based on the historic data provided by Singapore Pools.
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The datasets provided by TixCo are:
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# Profit & Loss (P&L) records
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This project strives to answer the main question:
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# Timetables of the events serviced
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* What are the factors which will affect the demand of an individual match?
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# Types of events
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** An example of such factors which can potentially impact the demand of sports event includes the day of the match, date of the match, time of the match, affluence or ranking of the teams involved and the number of matches being played concurrently.
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The data are presented in the form of Microsoft Excel worksheets and contains records over the time span of more than 6 years (Jan 2010 to May 2016).
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With this prediction, Singapore Pools is able to achieve the following benefits:
 
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# Improvements in resources allocation,
 
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# Better understanding of the factors that affects the demand of the matches,
 
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# Fraudulent prevention. For example, if there is a sudden spike in the demand of a match, there is a possibility of match-fixing.
 
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Singapore Pools is also keen to use this project as the foundation of future sports-related (e.g., motorsports) prediction models.
 
 
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Latest revision as of 08:01, 16 October 2016

Home

Team

Project Overview

Project Findings

Project Management

Documentation


Business Problem & Motivation

As TixCo is the only authorised ticketing service provider for certain events, it is crucial for them to cater to the demand of the mass public. Currently, TixCo is unable to anticipate the demand for the event organised and this post challenge for TixCo to fully capture the demand efficiently. As such, any uncaptured demand is an opportunity lost for TixCo. Hence, it is important for them to understanding the demand for an event.

For this, TixCo will need to:

  • Understand the trend and pattern of the number of tickets sold per event
  • Identifying the possible bottleneck for demand


Project Objective

The objective of this project is to:

  • Examine the underlying factors that affect the number of tickets sold per event
  • Understanding the distribution of the number of tickets sold per event
  • Understanding the relationship between the other attributes and the number of tickets sold per event

The project team also aims to develop an appropriate model to predict the number of tickets sold per event, based on the historical data provided by TixCo.


Datasets

The datasets provided by TixCo are:

  1. Profit & Loss (P&L) records
  2. Timetables of the events serviced
  3. Types of events

The data are presented in the form of Microsoft Excel worksheets and contains records over the time span of more than 6 years (Jan 2010 to May 2016).

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